Logistics and industrial automation increasingly rely on accurate localization to support and control manual and automated processes, with applications ranging from “smart things” through effective tracking and assistance solutions to robots such as automated guided vehicles (AGVs).
Ultra wideband (UWB) technology has been advocated as a localization solution suitable for asset tracking applications. Such applications are concerned with maintaining a centralized database of assets and their storage locations in a warehouse, hospital, or factory. When using UWB technology, assets, such as pallets, equipment, or also people may be equipped with tags that emit UWB signals at regular intervals. These signals may then be detected by UWB sensors installed in the warehouse, hospital, or factory. A central server then uses the UWB signals detected by the UWB sensors to compute the tag's location and update the centralized database.
Mobile robots are increasingly used to aid task performance in both consumer and industrial settings. Autonomous mobile robots in particular offer benefits including freeing workers from dirty, dull, dangerous, or distant tasks; high repeatability; and, in an increasing number of cases, also high performance. A significant challenge in the deployment of mobile robots in general and autonomous mobile robots in particular is robot localization, i.e., determining the robot's position in space. Current localization solutions are not well suited for many mobile robot applications, including applications where mobile robots operate in areas where global positioning system (GPS)-based localization is unreliable or inoperative, or applications that require operation near people.
Using current UWB localization solutions for robot localization would not enable a mobile robot to determine its own location directly. Rather, a robot equipped with a tag would first emit an UWB signal from its location, UWB sensors in its vicinity would then detect that UWB signal and relay it to a central server that would then compute the mobile robot's location, and then this location would have to be communicated back to the robot using a wireless link. This type of system architecture invariably introduces significant communication delays (e.g., latency) for controlling the mobile robot. This communication architecture also results in a relatively higher risk of lost signals (e.g., due to wireless interference) and correspondingly lower system robustness, which makes it unsuitable for many safety-critical robot applications (e.g., autonomous mobile robot operation). Furthermore, in this architecture the maximum number of tags and the tag emission frequency (i.e., the localization system's update rate) are invariably linked since multiple UWB signals may not overlap, which results in relatively lower redundancy (i.e., a limited number of tags allowed for an available network traffic load) and limited scalability (i.e., the system can only support a limited number of tags in parallel).
FIG. 2A is a block diagram overview of a centralized localization system as proposed in the prior art for use in asset tracking. In this system, tags 202 are moved within some environment, transmitting UWB signals 208 at various times. In this centralized system, mobile transmitters may operate independently and without synchronization. Stationary UWB sensors 204 are distributed throughout the environment. They have synchronized clocks. The UWB signals 208 transmitted by the tag 202 are received by the UWB sensors 204 that then communicate the signals' reception times to a centralized server 206. Based on the reception time at each UWB sensor 204, centralized server 206 computes the location of each tag 202. The system architecture shown in FIG. 2A is often advanced for asset tracking, where the location of all tags 202 should be known at a centralized location, and where tags 202 are not required to know their position. These properties make this system architecture unsuitable for situations where the objects being tracked are required to know their position; e.g., robots that make decisions based upon knowledge of their position. Furthermore, because each tag 202 is required to transmit signals 208, the update rate of the system is inversely proportional to the number of tags 202. This makes this system architecture unsuitable for situations where a large number of objects need to be tracked with a high update rate.
FIG. 2B is a block diagram overview of another localization system proposed in the prior art whereby mobile transceivers 252 communicate with stationary transceivers 254 through the two-way exchange of UWB signals 258. Such two-way communication with a stationary transceiver 254 enables the mobile transceiver 252 to compute the time-of-flight between itself and the stationary transceiver. In this architecture, communication between mobile transceivers 252 and stationary transceivers 254 must be coordinated, such that communications do not interfere. Knowledge of the time-of-flight to three or more stationary transceivers 254 enables each mobile transceiver 252 to compute its relative location within an environment using trilateration. Because each mobile transceiver 252 communicates with each stationary transceiver 254, the update rate of the system is inversely proportional to the number of mobile transceivers 252 and to the number of stationary transceivers 254. This architecture is therefore not suitable for systems where a large number of objects must be localized at a high frequency (e.g., tracking a group of robots, where position measurements are used in the robots' control loops to influence the robots' motions).